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Learning vector representations of words using neural networks has generated a strong enthusiasm in the NLP research community.
In particular, many contributions were proposed after the work of [1]}, [2]}, [3]} on training word embeddings.
The main reasons for this strong interest are: the proposal of a simple and efficient neural architecture to learn word vector representations, the availability of an open source tool Word2Vechttp://word2vec.googlecode.com/svn/trunk/ and the rapid structuring of a user communityhttps://groups.google.com/d/forum/word2vec-toolkit.
Later on, several contributions have extended the work of Mikolov on word vectors to phrases (sequences of words) [2]}, [5]} and to bilingual representations [6]}.
All these vector representations capture similarities between words, phrases or sentences at different levels (morphological, semantic).
